Learn A to Z of Algorithmic and Quantitative Trading
Quantra® is an e-learning portal by QuantInsti® that specializes in Algorithmic & Quantitative Trading. Quantra offers the best self-paced courses that are a mix of videos, audios, presentations, multiple choice questions and highly interactive exercises.
Made on Python version 2.7
This course will help you to understand the two major emotions that drive the entire market - Fear and Greed and help you to capitalize on these emotions to make profits.
- Creation of a strong foundation for the concepts on sentiment trading, sentiment indicators such as TRIN or Arms index or Short term Trading Index, Put/Call ratio and Volatility Index.
- How to interpret the sentiment indicators and devise trading strategies using sentiment indicators.
- Develop a trading logic by using signals from the sentiment indicators and code it using Python programming language.
- Analyze the strategy in Microsoft Excel using 2 years of historical data for backtesting.
- Unserstand the various risks involved in while trading using sentiment indicators.
- Market sentiments and how they influence your trade
- About sentiment indicators, how to interpret them and devise trading strategies based on these interpretations
- Code trading strategies in Python using sentiment indicators and analyze the trading signals generated by the strategy in Microsoft Excel
- Understand how to think logically about the entry and exit points in a trade, while creating an algorithm for trading
- Code trading strategies algorithmically and backtest on historical data to gauge your strategy’s performance in markets
- The Various risks which can influence your trade and how to mitigate them
Want to know more? Check out the course here.
The folder contains the following topics:
Fetching data: An exercise where you will learn how to fetch data for S&P 500 futures contracts, advancing stocks and declining stocks on NYSE from Quandl.
Defining Bollinger Bands: An exercise where you will learn to define the upper and lower Bollinger bands.
Identifying crossovers: An exercise where you will learn to set values for flags/boolean variables (variables storing either True or False) for moving average and stop loss band crossovers.
Generating Buy Signals: An exercise where you will learn to check the condition for opening a Buy position when the upper Bollinger band is crossed and assign an appropriate value to the variable ‘flag’.
The folder contains the following topics:
Generating a Sell Signal: An exercise where you will learn how to place a 'SELL' order if PCR crosses below the lower Bollinger band since it is a sign of an overbought market. We will take a contrarian position amid this bullish sentiment.
Closing an open sell position 1: An exercise where you will learn to place a buy order to close the open short position.
Closing an open sell position 2: An exercise where you will learn to close the open short position if PCR crosses the Lower Stoploss Band, i.e. PCR continues to fall after crossing the LBB, by placing a 'BUY' order, booking a loss.
Closing an open sell position 3: An exercise where you will learn to close an open short position by placing a buy order if the absolute stop loss value triggers a signal to close the position.
Closing an open Buy position: An exercise where you will learn to place a sell order to close the open buy position.
Generating a buy order: An exercise where you will learn how to generate an order to buy S&P 500 futures contracts, if the value of VIX exceeds the threshold value (i.e. thresh = 22).
Closing an open Buy position 1: An exercise where you will learn to check if the value of VIX exceeds the threshold value (i.e. thresh = 22); we will buy S&P 500 futures. In this exercise, if the S&P 500 futures contract value is trading 5% above its bought price, we will sell the contract and book a profit. We will then update the required fields and flags to avoid conflicts in our codes.
Closing an open Buy position 2: An exercise where you will learn to check if the value of VIX exceeds the threshold value (i.e. thresh = 22); then we will buy S&P 500 futures. If the S&P 500 futures value is 5% above its bought price, we will sell the futures contract and book a profit. In this exercise, if the S&P 500 futures value is 5% below its bought price, we will sell the futures contract and book a loss (our stoploss condition). We will then update the required fields and flags to avoid conflicts in our codes.
Appending trade data: An exercise where you will learn to use the append() method. The append() method adds a single item to an existing list. It does not return a new list, but it rather modifies the original list. We will modify the ‘stoploss' and ‘mtm’ list in this exercise.
You can understand the concepts by accessing the full course.
You can download the resources here.
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